Instructions to use mzhaoshuai/alpaca-7b-ref-bertscore with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mzhaoshuai/alpaca-7b-ref-bertscore with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mzhaoshuai/alpaca-7b-ref-bertscore")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mzhaoshuai/alpaca-7b-ref-bertscore", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mzhaoshuai/alpaca-7b-ref-bertscore with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mzhaoshuai/alpaca-7b-ref-bertscore"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mzhaoshuai/alpaca-7b-ref-bertscore",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mzhaoshuai/alpaca-7b-ref-bertscore

SGLang

How to use mzhaoshuai/alpaca-7b-ref-bertscore with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mzhaoshuai/alpaca-7b-ref-bertscore" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mzhaoshuai/alpaca-7b-ref-bertscore",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mzhaoshuai/alpaca-7b-ref-bertscore" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mzhaoshuai/alpaca-7b-ref-bertscore",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mzhaoshuai/alpaca-7b-ref-bertscore with Docker Model Runner:
```
docker model run hf.co/mzhaoshuai/alpaca-7b-ref-bertscore
```

RefAlign: RL with Similarity-based Rewards

GitHub repository: https://github.com/mzhaoshuai/RefAlign

Paper: Learning from Reference Answers: Versatile Language Model Alignment without Binary Human Preference Data.

This is the model aligned with RefAlign, a versatile REINFORCE-style alignment algorithm that utilizes language generation evaluation metrics (such as BERTScore) between sampled generations and reference answers as surrogate rewards.

It is primarily aligned for safety.

The training data is https://huggingface.co/datasets/mzhaoshuai/Llama-3.3-70B-Inst-awq_SafeRLHF.

When conducting Reinforcement Learning with Similarity-based Rewards, the reward function is BERTScore.